Kyle Polich - Common Errors in Machine Learning
The impact of machine learning on the modern world is indisputable. Yet, constructing a useful system requires careful consideration of one’s data set and interpretation of the model output. Mistakes in the creation of a model can easily yield a model that looks good but performs poorly in practice. This talk will explore some common pitfalls and how to avoid them.
By Kyle Polich - Principle Data Scientist at DataScience, Inc
At Southern California Data Science Conference Sept.25.2016 at USC
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